Cystic fibrosis (CF) is one of the most common genetic disorders in the U.S., affecting over 30,000 people in the U.S. and 70,000 worldwide. It is associated with increased mortality with a median lifespan of 41.1 years in the U.S. Mortality is highly correlated with long-term lung function decline. Although the gene responsible for CF (CFTR) was identified 25 years ago, lung disease remains a complex disease as CF patients with identical mutations may have dramatically different lung function, thus implying involvement of factors other than the CFTR gene itself. Family-based studies by us and others suggest that ~50% of variation in CF lung disease can be attributed to modifier genes and ~50% to environmental modifiers. Although a consortium-based genome-wide association study has identified several regions of interest associated with CF lung function, the identified loci cannot explain a large fraction of the variability of CF lung function, thus suggesting gene-environment (GxE) interactions may be present. It is important to identify these interactions as they may explain the variation seen in CF lung disease, provide additional insight into CF lung disease pathophysiology, and provide data for our long-term of goal of identifying individuals who could benefit most from specific environmental interventions. The primary goal of our research is to identify gene-environment (GxE) interactions in CF lung disease by analyzing existing data from 2086 patients with CF (The CF Twin-Sibling Study) with replication of key findings in a larger international group of ~5000 individuals with CF. We will test for GxE interactions with 3 known environmental modifiers of CF lung disease that could interact with genetic modifiers to account for unexplained variation in lung disease. These modifiers include Pseudomonas aeruginosa, which often colonizes the lungs of CF patients, a micro-environmental factor (secondhand tobacco smoke exposure from smokers living in the home), and a macro-environmental factor (climate: ambient temperature). To increase our ability to detect GxE effects, we propose to use methods new to CF and relevant to GWAS studies, specifically mixed modeling with longitudinal lung function measurements. Completion of this research will enhance our understanding of GxE interactions in CF and the use of mixed models for longitudinal data in other chronic diseases.